Jonathan J. Benn

A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study

A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects. The parameters of the model were based on experimental data from the literature describing insulin and carbohydrate absorption, renal loss of glucose, insulin-independent glucose utilisation and insulin-dependent glucose utilisation and production. The model can be adapted to the observed glucose metabolism in the individual patient and can be used to generate predicted 24-h blood glucose profiles. A penalty is assigned to each level of blood glucose, to indicate that high and low blood glucose levels are undesirable. The system can be asked to find the insulin doses that result in the most desirable 24-h blood glucose profile. In a series of 12 patients, the system predicted blood glucose with a mean error of 3.3 mmol/l. The insulin doses suggested by the system seemed reasonable and in several cases seemed more appropriate than the doses actually administered to the patients.

A model-based approach to insulin adjustment

A differential equation model of carbohydrate metabolism was implemented in the form of a causal probabilistic network. This permitted explicit represen-tations of the uncertainties associated with model based predictions of 24-hour blood glucose profiles. In addition, the implementation gave automatic learning and adjustment of model parameters based on measured blood glucose profiles. Insulin therapy was adjusted using a decision theoretical approach. Losses were assigned to blood glucose values that deviated from normal, and the insulin therapy was adjusted to minimize the expected total loss. The system was tested retrospectively on cases from 12 insulin dependent patients and seemed to compare favourably with clinical practice.

Casual probabilistic network modeling: an illustration of its role in the management of chronic diseases

This paper describes the role of the novel technique of causal probabilistic network (CPN) modeling as an approach to tackling control system problems typified by that of the administration of treatment to the patient suffering from a chronic disease such as diabetes. Three roles of a CPN are discussed. First, since diabetes arises as a consequence of impaired control of carbohydrate metabolism, the ability of a CPN to represent the uncertainty of a physiologically-based model is described. Second, its ability to make robust estimates of the parameters of the metabolic model is presented, and finally, in conjunction with decision theory approaches, its ability to compare alternative therapies and advise on insulin therapy for patients with insulin-dependent diabetes mellitus is illustrated.

Persistent abnormalities of the metabolism of an oral glucose load in insulin-treated type I diabetics.

We have compared disposal of an oral glucose load in 12 normal subjects and 10 c-peptide-negative, type I-diabetic subjects, who were treated with insulin (by overnight intravenous insulin infusion followed by a dose of subcutaneous insulin prior to the oral glucose load) to achieve a blood glucose profile that approximated the glucose intolerance commonly seen in insulin-treated diabetics. We used a combination of the dual-isotope and forearm techniques, together with whole-body indirect calorimetry, to quantify the various determinants of glucose tolerance. The diabetic subjects had impaired glucose tolerance in that, despite similar fasting plasma glucose levels (5.46 +/- 0.17 mmol/L v 5.35 +/- 0.10 mmol/L in the normal subjects), they had a higher peak glucose (14.3 +/- 1.2 mmol/L v 10.0 +/- 0.7 mmol/L P less than .01) and area under the glucose curve (2,483 +/- 197 mmol.min/L v 1,525 +/- 43 mmol.min/L P less than .001). Up to 120 minutes after the oral glucose load, the amount of glucose entering the systemic circulation exceeded that leaving by 14.6 +/- 2.3 g in the diabetics and only by 2.6 +/- 0.5 g in the normal subjects (P less than .001), accounting for the higher plasma glucose peak in the diabetics. Total systemic glucose appearance rates were significantly greater in the diabetics between 60 and 120 minutes, and endogenous glucose production suppressed more slowly in diabetics than in the normal subjects.(ABSTRACT TRUNCATED AT 250 WORDS)

Clinical Assessment of a Computer System for Insulin Dosage Adjustment

A computer system has been developed to provide advice on the day-to-day adjustment of carbohydrate intake and insulin regime in the diabetic patient. The prototype is intended to be used as a decision support system by clinical personnel in the context of day-to-day management of insulin-treated diabetic patients. It is designed for use during consultations, as a simulator of patient response following changed insulin and dietary regime and as a system to provide education on planning insulin therapy. Advice is generated by a qualitative knowledge based system which suggests what the next step in improving glycaemic control might be for a given patient, e.g. ‘decrease evening medium-acting insulin’. A clinical model is being developed to allow predictions of the patient’s blood glucose profile to be generated based on these adjustments. Clinical scenarios taken from postgraduate teaching cases have been used to compare the advice given by the computer with that of four independent diabetologists. The results of seven case studies are presented.

Glucose Turnover in Type I Diabetic Subjects During Exercise: Effect of selective and nonselective β-blockade and insulin withdrawal

OBJECTIVE To assess the effect of selective β1-blockade (atenolol and betaxolol) and nonselective β-blockade (propranolol) on glucose turnover in subjects with insulin-dependent (type I) diabetes mellitus during moderate exercise. RESEARCH DESIGN AND METHODS Five subjects with type I diabetes were infused with insulin and then exercised for 1 h, after pretreatment with each of the three drugs or saline and, on a separate day, after withdrawal of insulin. Glucose turnover was measured using tritiated glucose. RESULTS Plasma glucose, initially 9.2 ± 0.5 mmol/L (mean ± SE) when insulin infused and 14.0 ± 0.8 when insulin was withdrawn, fell on exercise by 3.4 ± 1.1 mmol/L (P < 0.05) saline, 4.0 ± 0.8 mmol/L (P < 0.01) with betaxolol, 3.8 ± 0.7 mmol/L (P < 0.01) with atenolol, 5.0 ± 0.6 mmol/L (P < 0.005) with propranolol, and 1.7 ± 1.0 mmol/L (NS) when insulin was withdrawn. Propranolol, but not the other β-blockers, caused a significantly greater fall in glucose on exercise than during the control study. Glucose appearance rate (Ra) was similar basally and rose to an almost identical level in all five groups during exercise. Glucose disappearance rate (Rd) rose similarly during exercise, except after propranolol when the rise was significantly greater with saline (P < 0.01). Failure of glucose to change significantly during exercise when insulin had been withdrawn was associated with the smallest rise in Rd and the highest nonesterified fatty acid concentrations. Propranolol and betaxolol, but not atenolol, reduced nonesterified fatty acids. CONCLUSIONS We conclude that the greater fall in glucose on exercise after β-blocking drugs is probably largely a direct effect of β2-blockade on muscle, increasing the exercise-induced rise in Rd glucose. This offers support to the use of β1-specific drugs, where β-blockade is necessary in type I diabetes.

A Causal Probabilistic Network Model Of Carbohydrate Metabolism For Insulin Therapy Adjustment

A differential equation model of carbohydrate metabolism was implemented in the form of a causal probabilistic network. This permitted explicit representations of the uncertainties associated with model based predictions of 24-hour blood glucose profiles. In addition the implementation gave automatic learning and adjustment of model parameters based on measured blood glucose profiles.Insulin therapy was adjusted using a decision theoretical approach. Penalties were assigned to blood glucose values that deviated from normal, and the insulin therapy was adjusted to minimize the expected Occurrence of penalties.